Sigortacılıkta Aktüeryal Fiyatlandırmaya Yeni Bir Bakış Açısı: Makine Öğrenmesi Temelli Yöntemler
Özet
Sigortacılıkta fiyatlandırma, risklerin doğru belirlenmesi ve primlerin adil şekilde tahmin edilmesi açısından kritik öneme sahiptir. Geleneksel aktüeryal ve istatistiksel yöntemler uzun yıllardır sigorta prim hesaplamalarında kullanılmaktadır. Özellikle Genelleştirilmiş Doğrusal Modeller (GDM) bu alanda yaygın biçimde tercih edilmektedir. Ancak büyük veri ve dijitalleşmenin hızla gelişmesi, makine öğrenmesi tabanlı yöntemlerin sigortacılıkta önemli bir alternatif olarak öne çıkmasına yol açmıştır. Bu bölümde, öncelikle klasik fiyatlandırma yaklaşımları ele alınmış; ardından karar ağaçları, rastgele ormanlar, destek vektör makineleri, yapay sinir ağları ve gradyan artırma gibi makine öğrenmesi algoritmalarının sigorta fiyatlandırmasındaki uygulamaları incelenmiştir. Makine öğrenmesi yöntemlerinin doğrusal olmayan karmaşık ilişkileri modelleme, büyük veri üzerinde yüksek performans gösterme ve gerçek zamanlı güncelleme yapabilme gibi avantajları olduğu; ancak yorumlanabilirlik, aşırı öğrenme ve yüksek hesaplama maliyeti gibi dezavantajlar da barındırdığı bu bölümde ortaya konulmuştur. Sonuç olarak, geleneksel yöntemlerle makine öğrenmesi yaklaşımlarının birbirini tamamlayıcı nitelikte olduğu, özellikle otomobil ve sağlık sigortalarında hibrit modellerin sektör için büyük potansiyel oluşturduğu görülmektedir.
Referanslar
Arıkan Tezergil, S. & Namsrai, G. (2018). Emeklilik matematiği ve aktüeryal maliyet yöntemleri. İstabul: Türkmen Kitapevi.
Ben Hamida, A., Kacem, M., de Peretti, C., & Belkacem, L. (2024). Machine learning based methods for ratemaking health care insurance. International Journal of Market Research, 66(6), 810-831.
Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2020). Machine learning in P&C insurance: A review for pricing and reserving. Risks, 9(1), 4.
Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (1997). Actuarial mathematics. Illinois: Society of Actuaries.
Brati, E., Braimllari, A., & Gjeçi, A. (2025). Machine learning applications for predicting high-cost claims using ınsurance data. Data, 10(6), 90.
Colella, S., & Jones, H. (2023). Machine learning and ratemaking: Assessing performance of four popular algorithms for modeling auto insurance pure premium, CAS E-Forum.
Corcoran, S., Graham, A. K., Arthur, W. B., & Peterson, D. W. (2012). A competitor’s strategy unclothed: How indirect measurement justified not fighting an insurance price war. 30th International Conference of the System Dynamics Society (pp. 2-28).
Cunha, L. & Bravo, J. M. (2022). Automobile usage-based-insurance: Improving risk management using telematics data. 17th Iberian Conference on Information Systems and Technologies.
Çetinkaya, T. (2019). Hayat sigortası prim üretimlerini tahminleme yöntemlerini karşılaştırarak gelecek yıllar prim üretimini tahminleme, Doktora tezi, Marmara Üniversitesi, Türkiye.
David, M. (2015). Auto insurance premium calculation using generalized linear models. Procedia Economics and Finance, 20, 147-156.
De Jong, P., & Heller, G. Z. (2008). Generalized linear models for insurance data. Cambridge University Press.
Degeneffe, M. (2020). A comparative analysis of statistical models for the pricing of health insurance. Network for Studies on Pension, Aging and Retirement, 1-53.
Denuit, M., Maréchal, X., Pitrebois, S., & Walhin, J. F. (2007). Actuarial modelling of claim counts: Risk classification, credibility and bonus-malus systems. John Wiley & Sons.
Dickson, D. C. (2004). Insurance risk and ruin. Cambridge University Press.
Dilmen, B., Gencer Ş., Arıkel F., Kayır, Ş. and Erdemir Ö. K. (2022). Yangın ve doğal afet sigortası priminin Box-Jenkins modelleri ve yapay sinir ağları ile tahmin edilmesi. İstatistikçiler Dergisi:İstatistik ve Aktüerya, 15 (2), 60–71.
Díaz Martínez Z., Fernández Menéndez J., & García Villalba L. J. (2023). Tariff analysis in automobile insurance: Is it time to switch from generalized linear models to generalized additive models? Mathematics, 11 (18), p. 3906.
Dugas, C., Bengio, Y., Chapados, N., Vincent, P., Denoncourt, G., & Fournier, C. (2003). Statistical learning algorithms applied to automobile insurance ratemaking. CAS Forum, 1 (1), 179-214.
Fu, D., Hong, Q., Xu, X. (2024). Navigating insurance challenges: a decision tree model for insurance pricing and risk strategy, Highlights in Business, Economics and Management, 33, 699-706.
Goldburd, M., Khare, A., Tevet, D., & Guller, D. (2016). Generalized linear models for insurance rating. CAS Monographs Series, 5.
Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659-3667.
Guelman, L., Guillén, M., & Pérez-Marín, A. M. (2012). Random forests for uplift modeling: An insurance customer retention case. International Conference on Modeling and Simulation in Engineering, Economics and Management, Heidelberg: Springer Berlin, 123-133.
Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice Hall PTR.
Hanafy, M., & Ming, R. (2021). Machine learning approaches for auto insurance big data. Risks, 9(2), 42.
Haji Mohammad, F. (2023). Insurance Premium Calculation Using Machine Learning Methodologies, Doctoral dissertation, Carleton University.
Henckaerts, R., Côté, M. P., Antonio, K., & Verbelen, R. (2021). Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal, 25(2), 255-285.
Herzog, T. L. (1999). Introduction to Credibility Theory, Third adition, ACTEX. Abington.
Hickman, J. C. (1997). Introduction to actuarial modeling. North American Actuarial Journal, 1(3), 1-5.
Holvoet, F., Antonio, K., & Henckaerts, R. (2025). Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff. North American Actuarial Journal, 1-44.
Hossack, I. B., Pollard, J. H., & Zehnwirth, B. (1999). Introductory statistics with applications in general insurance. Cambridge University Press.
Jain, N. (2018). Towards machine learning: Alternative methods for insurance pricing poisson-gamma GLM’s, tweedie GLM’s and artificial neural networks, Institute and Faculty of Actuaries.
Kangro, R. & Pärna, K. (2011). K-nearest neighbors as pricing tool in insurance, Proceedings of the Ninth Tartu Conference on Multivariate Statistics & The 20thInternational Workshop on Matrices and Statistics, Estonia.
Kartasheva, A. V., & Traskin, M. (2015). Insurers' insolvency prediction using random forest classification. SSRN.
Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss models: from data to decisions, John Wiley & Sons.
Kumar, R., Rakhra M., Prashar D., Upadhyay S., Mrsic L. & Khan A. A. (2024). Machine learning and ratemaking evaluation of four auto insurance pure premium modeling algorithms, 28th International Computer Science and Engineering Conference, Khon Kaen, Thailand, pp. 1-6.
Kuo, K. (2020). Towards explainability of machine learning models in insurance pricing, Variance.
Larson, R. B. (2019). Promoting demand-based pricing. Journal of Revenue and Pricing Management, 18(1), 42-51.
McCullagh, P. & Nelder, J. A. (1989). Generalized linear models. Chapman and Hall, New York.
Menge, O.W. & Fischer, C.H. (1991). The mathematics of life insurance, The MacMillan Company.
Mossin, J. (1968). Aspects of Rational Insurance Purchasing, Journal of Political Economy, 76(4), 553–568.
Naufal, N., Devila, S., & Lestari, D. (2019). Generalized linear model (GLM) to determine life insurance premiums. AIP Conference Proceedings, 2168 (1), AIP Publishing.
Nomer, C., & Yunak, H. (2000). Sigortanın genel prensipleri, Ceyma Matbacılık.
Panjee, P., & Amornsawadwatana, S. (2024). A generalized linear model and machine learning approach for predicting the frequency and severity of Cargo insurance in Thailand’s border trade context. Risks, 12(2), 25.
Patra, G. K., Kuraku, C., Konkimalla, S., Boddapati, V. N., Sarisa, M., & Reddy, M. S. (2024). An analysis and prediction of health insurance costs using machine learning-based regressor techniques. Journal of Data Analysis and Information Processing, 12(4), 581-596.
Prova, N. N. I. (2024). Advanced machine learning techniques for predictive analysis of health insurance. Second International Conference on Intelligent Cyber Physical Systems and Internet of Things, pp. 1166-1170.
Ohlsson E. & Johansson, B. (2015). Non-life insurance pricing with generalized linear models, Springer, Berlin.
Omerašević, A., & Selimović, J. (2020). Classification ratemaking using decision tree in the insurance market of Bosnia and Herzegovina. The South East European Journal of Economics and Business, 15(2), 124-139.
Orji, U., & Ukwandu, E. (2024). Machine learning for an explainable cost prediction of medical insurance. Machine Learning with Applications, 15, 100516.
Özaltın, Ö., Çobanbaş, S., Sırakaya, Y. & Güneş, Y. (2025). Alzheimer hastalığının manyetik rezonans görüntülerden hibrit derin öğrenme yaklaşımı ile otomatik tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 37(1), 321-339.
Rahmawati, T., Susanti, D., & Riaman, R. (2023). Determining pure premium of motor vehicle insurance with generalized linear models (GLM). International Journal of Quantitative Research and Modeling, 4(4), 207-214.
Rasinojehdehi, R., & Azizi, S. (2023). Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance. Big Data and Computing Visions, 3(3), 125-136.
Reil, J. P. C. (2024). Beyond generalized linear models: Advancing insurance pricing through ınterpretable and explainable machine learning. Master's thesis, University of Twente.
Rejda, G. E. (2005). Principles of Risk Management and Insurance. (Erişah ARICAN, Çev. Ed.). Ankara: Nobel Yayınevi.
Rustam, Z., & Ariantari, N. P. A. A. (2018). Support vector machines for classifying policyholders satisfactorily in automobile insurance. Journal of Physics: Conference Series, 1028 (1), p. 012005.
Sarıaslan, M. (2007). Tahsilat ve Teknik Kârlılığı Ölçen Rasyolar. Reasürör Dergisi.
Selvakumar, V., Satpathi, D. K., Kumar, P. P., & Haragopal, V. V. (2021). Predictive modeling of insurance claims using machine learning approach for different types of motor vehicles. Accounting and Finance, 9(1), 1-14.
Sherwood, M. T. (2001). Individual Risk Rating. Casualty Actuarial Society.
Spedicato, G., Dutang, C., & Petrini, L. (2018). Machine learning methods to perform pricing optimization: A comparison with standard generalized linear models. Variance, 12(1), 69-89.
Staudt, Y., & Wagner, J. (2021). Assessing the performance of random forests for modeling claim severity in collision car insurance. Risks, 9(3), 53.
Sorhun, E. (2021). Machine Learning with Python. İstanbul: Abakus Yayıncılık.
Treetanthiploet, T., Zhang, Y., Szpruch, L., Bowers-Barnard, I., Ridley, H., Hickey, J. & Pearce, C. (2023). Insurance pricing on price comparison websites via reinforcement learning. arXiv preprint arXiv:2308.06935.
Tse, Y. K. (2009). Nonlife actuarial models. Theory, methods and evaluation. Cambridge University Press.
Ulusoy, İ. (2023). Yapay zeka ve makine öğrenmesi sigorta sektörünü yeniden şekillendiriyor. Türkiye Sigorta Birliği.
Virgilis, M. D., Lupton D., McGrath, L., Qazvini, M., Roby, S. & Vernon, L., (2022). Machine learning in insurance, Casualty Actuarial Society E-Forum.
Wilson, A. A., Nehme, A., Dhyani, A., & Mahbub, K. (2024). A comparison of generalised linear modelling with machine learning approaches for predicting loss cost in motor insurance. Risks, 12(4), 62.
Woodard, J. D., Sherrick, B. J., & Schnitkey, G. D. (2011). Actuarial impacts of loss cost ratio ratemaking in US crop insurance programs. Journal of Agricultural and Resource Economics, 211-228.
Wüthrich, M. V., & Merz, M. (2023). Statistical foundations of actuarial learning and its applications. Springer Nature.
Yang, Y., Qian, W., & Zou, H. (2018). Insurance premium prediction via gradient tree-boosted Tweedie compound Poisson models. Journal of Business & Economic Statistics, 36(3), 456-470.
Yeo, A. C., Smith, K. A., Willis, R. J., & Brooks, M. (2001). Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry. Intelligent Systems in Accounting, Finance & Management, 10(1), 39-50.
Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11), 218.
Zhang, M. L., & Zhou, Z. H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038-2048.
Zhang, Y., Ji, L., Aivaliotis, G., & Taylor, C. (2024). Bayesian CART models for insurance claims frequency. Insurance: Mathematics and Economics, 114, 108-131.
Referanslar
Arıkan Tezergil, S. & Namsrai, G. (2018). Emeklilik matematiği ve aktüeryal maliyet yöntemleri. İstabul: Türkmen Kitapevi.
Ben Hamida, A., Kacem, M., de Peretti, C., & Belkacem, L. (2024). Machine learning based methods for ratemaking health care insurance. International Journal of Market Research, 66(6), 810-831.
Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2020). Machine learning in P&C insurance: A review for pricing and reserving. Risks, 9(1), 4.
Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (1997). Actuarial mathematics. Illinois: Society of Actuaries.
Brati, E., Braimllari, A., & Gjeçi, A. (2025). Machine learning applications for predicting high-cost claims using ınsurance data. Data, 10(6), 90.
Colella, S., & Jones, H. (2023). Machine learning and ratemaking: Assessing performance of four popular algorithms for modeling auto insurance pure premium, CAS E-Forum.
Corcoran, S., Graham, A. K., Arthur, W. B., & Peterson, D. W. (2012). A competitor’s strategy unclothed: How indirect measurement justified not fighting an insurance price war. 30th International Conference of the System Dynamics Society (pp. 2-28).
Cunha, L. & Bravo, J. M. (2022). Automobile usage-based-insurance: Improving risk management using telematics data. 17th Iberian Conference on Information Systems and Technologies.
Çetinkaya, T. (2019). Hayat sigortası prim üretimlerini tahminleme yöntemlerini karşılaştırarak gelecek yıllar prim üretimini tahminleme, Doktora tezi, Marmara Üniversitesi, Türkiye.
David, M. (2015). Auto insurance premium calculation using generalized linear models. Procedia Economics and Finance, 20, 147-156.
De Jong, P., & Heller, G. Z. (2008). Generalized linear models for insurance data. Cambridge University Press.
Degeneffe, M. (2020). A comparative analysis of statistical models for the pricing of health insurance. Network for Studies on Pension, Aging and Retirement, 1-53.
Denuit, M., Maréchal, X., Pitrebois, S., & Walhin, J. F. (2007). Actuarial modelling of claim counts: Risk classification, credibility and bonus-malus systems. John Wiley & Sons.
Dickson, D. C. (2004). Insurance risk and ruin. Cambridge University Press.
Dilmen, B., Gencer Ş., Arıkel F., Kayır, Ş. and Erdemir Ö. K. (2022). Yangın ve doğal afet sigortası priminin Box-Jenkins modelleri ve yapay sinir ağları ile tahmin edilmesi. İstatistikçiler Dergisi:İstatistik ve Aktüerya, 15 (2), 60–71.
Díaz Martínez Z., Fernández Menéndez J., & García Villalba L. J. (2023). Tariff analysis in automobile insurance: Is it time to switch from generalized linear models to generalized additive models? Mathematics, 11 (18), p. 3906.
Dugas, C., Bengio, Y., Chapados, N., Vincent, P., Denoncourt, G., & Fournier, C. (2003). Statistical learning algorithms applied to automobile insurance ratemaking. CAS Forum, 1 (1), 179-214.
Fu, D., Hong, Q., Xu, X. (2024). Navigating insurance challenges: a decision tree model for insurance pricing and risk strategy, Highlights in Business, Economics and Management, 33, 699-706.
Goldburd, M., Khare, A., Tevet, D., & Guller, D. (2016). Generalized linear models for insurance rating. CAS Monographs Series, 5.
Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659-3667.
Guelman, L., Guillén, M., & Pérez-Marín, A. M. (2012). Random forests for uplift modeling: An insurance customer retention case. International Conference on Modeling and Simulation in Engineering, Economics and Management, Heidelberg: Springer Berlin, 123-133.
Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice Hall PTR.
Hanafy, M., & Ming, R. (2021). Machine learning approaches for auto insurance big data. Risks, 9(2), 42.
Haji Mohammad, F. (2023). Insurance Premium Calculation Using Machine Learning Methodologies, Doctoral dissertation, Carleton University.
Henckaerts, R., Côté, M. P., Antonio, K., & Verbelen, R. (2021). Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal, 25(2), 255-285.
Herzog, T. L. (1999). Introduction to Credibility Theory, Third adition, ACTEX. Abington.
Hickman, J. C. (1997). Introduction to actuarial modeling. North American Actuarial Journal, 1(3), 1-5.
Holvoet, F., Antonio, K., & Henckaerts, R. (2025). Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff. North American Actuarial Journal, 1-44.
Hossack, I. B., Pollard, J. H., & Zehnwirth, B. (1999). Introductory statistics with applications in general insurance. Cambridge University Press.
Jain, N. (2018). Towards machine learning: Alternative methods for insurance pricing poisson-gamma GLM’s, tweedie GLM’s and artificial neural networks, Institute and Faculty of Actuaries.
Kangro, R. & Pärna, K. (2011). K-nearest neighbors as pricing tool in insurance, Proceedings of the Ninth Tartu Conference on Multivariate Statistics & The 20thInternational Workshop on Matrices and Statistics, Estonia.
Kartasheva, A. V., & Traskin, M. (2015). Insurers' insolvency prediction using random forest classification. SSRN.
Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss models: from data to decisions, John Wiley & Sons.
Kumar, R., Rakhra M., Prashar D., Upadhyay S., Mrsic L. & Khan A. A. (2024). Machine learning and ratemaking evaluation of four auto insurance pure premium modeling algorithms, 28th International Computer Science and Engineering Conference, Khon Kaen, Thailand, pp. 1-6.
Kuo, K. (2020). Towards explainability of machine learning models in insurance pricing, Variance.
Larson, R. B. (2019). Promoting demand-based pricing. Journal of Revenue and Pricing Management, 18(1), 42-51.
McCullagh, P. & Nelder, J. A. (1989). Generalized linear models. Chapman and Hall, New York.
Menge, O.W. & Fischer, C.H. (1991). The mathematics of life insurance, The MacMillan Company.
Mossin, J. (1968). Aspects of Rational Insurance Purchasing, Journal of Political Economy, 76(4), 553–568.
Naufal, N., Devila, S., & Lestari, D. (2019). Generalized linear model (GLM) to determine life insurance premiums. AIP Conference Proceedings, 2168 (1), AIP Publishing.
Nomer, C., & Yunak, H. (2000). Sigortanın genel prensipleri, Ceyma Matbacılık.
Panjee, P., & Amornsawadwatana, S. (2024). A generalized linear model and machine learning approach for predicting the frequency and severity of Cargo insurance in Thailand’s border trade context. Risks, 12(2), 25.
Patra, G. K., Kuraku, C., Konkimalla, S., Boddapati, V. N., Sarisa, M., & Reddy, M. S. (2024). An analysis and prediction of health insurance costs using machine learning-based regressor techniques. Journal of Data Analysis and Information Processing, 12(4), 581-596.
Prova, N. N. I. (2024). Advanced machine learning techniques for predictive analysis of health insurance. Second International Conference on Intelligent Cyber Physical Systems and Internet of Things, pp. 1166-1170.
Ohlsson E. & Johansson, B. (2015). Non-life insurance pricing with generalized linear models, Springer, Berlin.
Omerašević, A., & Selimović, J. (2020). Classification ratemaking using decision tree in the insurance market of Bosnia and Herzegovina. The South East European Journal of Economics and Business, 15(2), 124-139.
Orji, U., & Ukwandu, E. (2024). Machine learning for an explainable cost prediction of medical insurance. Machine Learning with Applications, 15, 100516.
Özaltın, Ö., Çobanbaş, S., Sırakaya, Y. & Güneş, Y. (2025). Alzheimer hastalığının manyetik rezonans görüntülerden hibrit derin öğrenme yaklaşımı ile otomatik tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 37(1), 321-339.
Rahmawati, T., Susanti, D., & Riaman, R. (2023). Determining pure premium of motor vehicle insurance with generalized linear models (GLM). International Journal of Quantitative Research and Modeling, 4(4), 207-214.
Rasinojehdehi, R., & Azizi, S. (2023). Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance. Big Data and Computing Visions, 3(3), 125-136.
Reil, J. P. C. (2024). Beyond generalized linear models: Advancing insurance pricing through ınterpretable and explainable machine learning. Master's thesis, University of Twente.
Rejda, G. E. (2005). Principles of Risk Management and Insurance. (Erişah ARICAN, Çev. Ed.). Ankara: Nobel Yayınevi.
Rustam, Z., & Ariantari, N. P. A. A. (2018). Support vector machines for classifying policyholders satisfactorily in automobile insurance. Journal of Physics: Conference Series, 1028 (1), p. 012005.
Sarıaslan, M. (2007). Tahsilat ve Teknik Kârlılığı Ölçen Rasyolar. Reasürör Dergisi.
Selvakumar, V., Satpathi, D. K., Kumar, P. P., & Haragopal, V. V. (2021). Predictive modeling of insurance claims using machine learning approach for different types of motor vehicles. Accounting and Finance, 9(1), 1-14.
Sherwood, M. T. (2001). Individual Risk Rating. Casualty Actuarial Society.
Spedicato, G., Dutang, C., & Petrini, L. (2018). Machine learning methods to perform pricing optimization: A comparison with standard generalized linear models. Variance, 12(1), 69-89.
Staudt, Y., & Wagner, J. (2021). Assessing the performance of random forests for modeling claim severity in collision car insurance. Risks, 9(3), 53.
Sorhun, E. (2021). Machine Learning with Python. İstanbul: Abakus Yayıncılık.
Treetanthiploet, T., Zhang, Y., Szpruch, L., Bowers-Barnard, I., Ridley, H., Hickey, J. & Pearce, C. (2023). Insurance pricing on price comparison websites via reinforcement learning. arXiv preprint arXiv:2308.06935.
Tse, Y. K. (2009). Nonlife actuarial models. Theory, methods and evaluation. Cambridge University Press.
Ulusoy, İ. (2023). Yapay zeka ve makine öğrenmesi sigorta sektörünü yeniden şekillendiriyor. Türkiye Sigorta Birliği.
Virgilis, M. D., Lupton D., McGrath, L., Qazvini, M., Roby, S. & Vernon, L., (2022). Machine learning in insurance, Casualty Actuarial Society E-Forum.
Wilson, A. A., Nehme, A., Dhyani, A., & Mahbub, K. (2024). A comparison of generalised linear modelling with machine learning approaches for predicting loss cost in motor insurance. Risks, 12(4), 62.
Woodard, J. D., Sherrick, B. J., & Schnitkey, G. D. (2011). Actuarial impacts of loss cost ratio ratemaking in US crop insurance programs. Journal of Agricultural and Resource Economics, 211-228.
Wüthrich, M. V., & Merz, M. (2023). Statistical foundations of actuarial learning and its applications. Springer Nature.
Yang, Y., Qian, W., & Zou, H. (2018). Insurance premium prediction via gradient tree-boosted Tweedie compound Poisson models. Journal of Business & Economic Statistics, 36(3), 456-470.
Yeo, A. C., Smith, K. A., Willis, R. J., & Brooks, M. (2001). Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry. Intelligent Systems in Accounting, Finance & Management, 10(1), 39-50.
Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine, 4(11), 218.
Zhang, M. L., & Zhou, Z. H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038-2048.
Zhang, Y., Ji, L., Aivaliotis, G., & Taylor, C. (2024). Bayesian CART models for insurance claims frequency. Insurance: Mathematics and Economics, 114, 108-131.
